keyword

underwater color image quality evaluation

Underwater color image quality evaluation is the process of measuring and assessing the visual quality and fidelity of color images captured in aquatic environments. Because light traveling through water undergoes wavelength-dependent absorption and scattering, underwater imagery typically suffers from severe degradations such as color cast, reduced contrast, low brightness, and blurred details. This evaluation encompasses subjective assessments based on human visual perception as well as objective computational metrics, including full-reference methods that compare processed outputs to ground-truth images and no-reference metrics tailored to quantify underwater characteristics such as chroma variance, saturation, and luminance contrast. By quantifying image clarity and color authenticity, underwater color image quality evaluation provides essential benchmarks for developing, tuning, and validating underwater image restoration, enhancement algorithms, and marine robotic vision systems.

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An Underwater Image Enhancement Benchmark Dataset and Beyond

Chongyi Li, Chunle Guo, Wenqi Ren, Runmin Cong, Junhui Hou, Sam Kwong, Dacheng Tao

OrganizationsBeijing Jiaotong UniversityCity University of Hong KongInstitute of Information Engineering, Chinese Academy of SciencesTianjin UniversityUniversity of Sydney

Why you should read this

Establishes a standardized real-world benchmark dataset containing 950 underwater images alongside a convolutional baseline network, Water-Net, to rigorously evaluate and advance underwater image restoration.

Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large-scale real-world images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images. We treat the rest 60 underwater images which cannot obtain satisfactory reference images as challenging data. Using this dataset, we conduct a comprehensive study of the state-of-the-art underwater image enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater image enhancement. The dataset and code are available at this https URL.

Source

https://arxiv.org/pdf/1901.05495

Added

2026-09-18